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Spatial join aggregate (SJA) is a commonly used but time-consuming operation in spatial database. Since it involves both the spatial join and the aggregate operation, performing SJA is a challenging task especially facing the deluge of spatial data. A popular model nowadays for massive data processing is the shared-nothing cluster using MapReduce.
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Figure 2.2: Representation of temporal data - "Range aggregate processing in spatial databases"
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W e first review the range aggregate processing methods. in spatial databases. The range aggregate (RA) query was. proposed for the scenario where users are interested in sum-
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The incremental nearest neighbor algorithm significantly outperforms the existing k-nearest neighbor algorithm for distance browsing queries in a spatial database that uses the R-tree as a spatial index and it is proved informally that at any step in its execution the incremental nearest neighbors algorithm is optimal with respect to the …
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Contribute to dihog/sbm development by creating an account on GitHub.
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Despite the existence of obstacles in many database applications, traditional spatial query processing assumes that points in space are directly reachable and utilizes the Euclidean distance metric.
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This paper studies spatial indexes that solve such queries efficiently and proposes the aggregate Point-tree (aP-tree), which achieves logarithmic cost to the data set …
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Figure 4.6: Bulkloading the aP-tree using pooling pages - "Range aggregate processing in spatial databases" ... "Range aggregate processing in spatial databases" Skip to search form Skip to main content Skip to account menu. Semantic Scholar's Logo. Search 217,476,902 papers from all fields of science.
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spatial aggregates is devoted to mechanisms to support range queries, or box queries. Aggregate range queries perform some aggregate operation over spatial or …
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This paper studies spatial indexes that solve such queries efficiently and proposes the aggregate Point-tree (aP-tree), which achieves logarithmic cost to the data set …
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Range Aggregate Processingin Spatial DatabasesYufei Tao and Dimitris PapadiasAbstract—A range aggregate query returns summarized information about the points f ... CSCI 8715 - Range Aggregate Processing in Spatial Databases School name University of Minnesota- Twin Cities ...
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In this paper, we solve the maximizing range sum (MaxRS ) problem in spatial databases. Given a set O of weighted points (a.k.a. objects) and a rectangle r of a given size, the goal of the MaxRS problem is to nd a location of r which maximizes the sum of the weights of all the objects covered by r.
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In spatial database outsourcing, a data owner delegates its data management tasks to a location-based service (LBS), which indexes the data with an authenticated data structure (ADS). The LBS receives queries (ranges, nearest neighbors) originating from several clients/subscribers. ... Range aggregate processing in spatial databases Author(s ...
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Relational databases have been around for a long time and spatial databases have exploited this feature for close to two decades. The recent past has seen the development of NoSQL non-relational databases, which are now being adopted for spatial object storage and handling, too. While SQL databases face scalability and agility challenges …
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Figure 2.1: The aR-tree - "Range aggregate processing in spatial databases"
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Processing aggregate range queries on remote spatial databases suffers from accessing huge and/or large number of databases that operate autonomously and simple and/or restrictive web API interfaces. To overcome these difficulties, this paper applies a ...
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In this paper, we define the Federated Range Aggregation (FRA) problem and investigate efficient solutions to range aggregation queries over large-scale spatial data federation. Observing that the underlying applications demand real-time. 2375-026X/22/$31.00 ©2022 IEEE DOI 10.1109/ICDE53745.2022.00156. response of high-frequency queries while ...
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A range aggregate query returns summarized information about the points falling in a hyper-rectangle (e.g., the total number of these points instead of their concrete ids). This paper studies spatial indexes that solve such queries efficiently and proposes the aggregate Point-tree (aP-tree), which achieves logarithmic cost to the data set …
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Processing aggregate range queries on remote spatial databases suffers from accessing huge and/or large number of databases that operate autonomously and simple and/or restrictive web API interfaces. To overcome these difficulties, this paper applies a revised version of regular polygon-based search algorithm (RPSA) to …
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A novel accurate prediction index technique, named PRA-tree, is presented, which takes into account both the velocity and space distribution of moving objects and is supplemented by a hash index on IDs ofMoving objects, thus having a good dynamic performance and concurrency. Predicted range aggregate (PRA) query is an important researching issue …
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queries, or box queries. Aggregate range queries perform some aggregate operation over spatial or spatiotemporal data that fall into a user speci ed area (the range or box), pos-sibly over some speci ed time window [17, 10, 13]. Such aggregation mechanisms seem to stem from the support for range queries provided by spatial indexing methods such as
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The MR-tree is introduced, a space-efficient ADS that supports fast query processing and verification and the MR*-tree, a modified version of the MR- tree, which significantly reduces the VO size through a novel embedding technique. In spatial database outsourcing, a data owner delegates its data management tasks to a location …
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In this paper, we identify and solve a new type of spatial queries, called continuous visible nearest neighbor (CVNN) search. Given a data set P, an obstacle set O, and a query line segment q in a two-dimensional space, a CVNN query returns a set of $${langle p, Rrangle}$$ tuples such that $${p in P}$$ is the nearest neighbor to …
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Supporting aggregate range queries on remote spatial databases suffers from 1) huge and/or large numbers of databases, and 2) limited type of access interfaces.
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We consider variations of the standard orthogonal range searching motivated by applications in database querying and VLSI layout processing. In a generic instance of such a problem, called a range-aggregate query problem we wish to preprocess a set S of geometric objects such that given a query orthogonal range q, a certain intersection or …
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This paper investigates the MaxRS problem in spatial databases. Given a set O of weighted points and a rectangular region r of a given size, the goal of the MaxRS problem is to find a location of r such that the sum of the weights of all the points covered by r is maximized. This problem is useful in many location-based applications such as …
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This paper attempts to evaluate the performance of an existing NoSQL database 'MongoDB' with its inbuilt spatial functions with that of a SQL database with spatial extension 'PostGIS' for two problems – spatial and aggregate queries, across a range of datasets, with varying features counts.
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A scalable algorithm for maximizing range sum in spatial databases. A scalable algorithm for maximizing range sum in spatial databases. Chin-Wan Chung. 2012, Proceedings of the VLDB Endowment. See Full PDF Download PDF.
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In this paper, we solve the maximizing range sum (MaxRS) problem in spatial databases. Given a set O of weighted points (a.k.a. objects) and a rectangle r of a given size, the …
DetailsMaximum Range-Sum (MaxRS) query is an important operator in spatial database for retrieving regions of interest (ROIs). Given a rectangular query size a × b and a set of spatial objects associated with positive weights, MaxRS retrieves rectangular regions Q of size a × b, such that the sum of object weights covered by Q (i.e., range-sum) is …
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Range aggregation queries over spatial data returns sum-marized information about the spatial objects falling within a spatial range specified as either a circle or a rectangle [1], …
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The R-tree is known to be one of the most popular index structures to efficiently process window queries in spatial databases. Intuitively, the aggregate R-tree (aR-tree) [7], [10] improves the R-tree's performance in range sum queries by storing, in each intermediate entry, pre-aggregated sums of the objects in the subtree. Fig. 1 …
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A revised version of regular polygon-based search algorithm RPSA is applied to approximately search aggregate range query results over remote spatial databases and results show that precision is over 0.97 with regard to sumrange query results and NOR is at most 4.3. Processing aggregate range queries on remote spatial databases suffers …
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Supporting aggregate range queries on remote spatial databases suffers from 1) huge and/or large numbers of databases, and 2) limited type of access interfaces. This paper …
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